Software

This class will involve a number of computational homework assignments. These must be completed in quarto markdown files using the python language and be accompanied by a rendered pdf file. Teaching fluency in both R and python is an important goal of the program, and this course is devoted partially to building your python competency as it is a very standard language for machine learning applications.

Guide to Key Packages:

• sklearn is the most commonly used machine learning library in python
• statsmodels is a useful package for statistics in python
• quarto Document and website authoring app. Used for your homework and maybe project
• pytorch is a package for neural networks in python
• numpy Basic numerical python package
• pymc is a basic package for Bayesian stats in python
• marginaleffects is a very useful package for interpreting and communicating machine learning models in python
• matplotlib is a powerful plotting library in python
• seaborn is another powerful plotting library, though technically powered by matplotlib.
• islp Python package with the datasets of our textbook
• conda A good python package and environment manager
• mamba A fast drop in replacement for conda’s package installer
• pandas Standard python package for wrangling data
• polars Blazingly fast data wrangling library intended for larger datasets
• shiny Made initially for R but it works with python now. Deploy ML apps
• streamlit Package for making lightweight apps
• FastAPI Another lightweight production library